We tackle the problem of generating code snippets from natural language descriptions using the CoNaLa dataset. We use the self-attention based transformer architecture and show that it performs better than recurrent attention-based encoder decoder. Furthermore, we develop a modified form of back translation and use cycle consistent losses to train the model in an end-to-end fashion. We achieve a BLEU score of 16.99 beating the previously reported baseline of the CoNaLa challenge.
@article{arxiv.2202.00367,
title = {Natural Language to Code Using Transformers},
author = {Uday Kusupati and Venkata Ravi Teja Ailavarapu},
journal= {arXiv preprint arXiv:2202.00367},
year = {2022}
}